Update files from the datasets library (from 1.0.0)

Release notes: https://github.com/huggingface/datasets/releases/tag/1.0.0

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bin.* filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

civil_comments.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""CivilComments from Jigsaw Unintended Bias Kaggle Competition."""
+
+from __future__ import absolute_import, division, print_function
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """
+@article{DBLP:journals/corr/abs-1903-04561,
+  author    = {Daniel Borkan and
+               Lucas Dixon and
+               Jeffrey Sorensen and
+               Nithum Thain and
+               Lucy Vasserman},
+  title     = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
+               Classification},
+  journal   = {CoRR},
+  volume    = {abs/1903.04561},
+  year      = {2019},
+  url       = {http://arxiv.org/abs/1903.04561},
+  archivePrefix = {arXiv},
+  eprint    = {1903.04561},
+  timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
+  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+_DESCRIPTION = """
+The comments in this dataset come from an archive of the Civil Comments
+platform, a commenting plugin for independent news sites. These public comments
+were created from 2015 - 2017 and appeared on approximately 50 English-language
+news sites across the world. When Civil Comments shut down in 2017, they chose
+to make the public comments available in a lasting open archive to enable future
+research. The original data, published on figshare, includes the public comment
+text, some associated metadata such as article IDs, timestamps and
+commenter-generated "civility" labels, but does not include user ids. Jigsaw
+extended this dataset by adding additional labels for toxicity and identity
+mentions. This data set is an exact replica of the data released for the
+Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge.  This
+dataset is released under CC0, as is the underlying comment text.
+"""
+
+_DOWNLOAD_URL = "https://storage.googleapis.com/jigsaw-unintended-bias-in-toxicity-classification/civil_comments.zip"
+
+
+class CivilComments(datasets.GeneratorBasedBuilder):
+    """Classification and tagging of 2M comments on news sites.
+
+    This version of the CivilComments Dataset provides access to the primary
+    seven labels that were annotated by crowd workers, the toxicity and other
+    tags are a value between 0 and 1 indicating the fraction of annotators that
+    assigned these attributes to the comment text.
+
+    The other tags, which are only available for a fraction of the input examples
+    are currently ignored, as are all of the attributes that were part of the
+    original civil comments release. See the Kaggle documentation for more
+    details about the available features.
+    """
+
+    VERSION = datasets.Version("0.9.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "text": datasets.Value("string"),
+                    "toxicity": datasets.Value("float32"),
+                    "severe_toxicity": datasets.Value("float32"),
+                    "obscene": datasets.Value("float32"),
+                    "threat": datasets.Value("float32"),
+                    "insult": datasets.Value("float32"),
+                    "identity_attack": datasets.Value("float32"),
+                    "sexual_explicit": datasets.Value("float32"),
+                }
+            ),
+            # The supervised_keys version is very impoverished.
+            supervised_keys=("text", "toxicity"),
+            homepage="https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filename": os.path.join(dl_path, "train.csv"), "toxicity_label": "target"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filename": os.path.join(dl_path, "test_public_expanded.csv"),
+                    "toxicity_label": "toxicity",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filename": os.path.join(dl_path, "test_private_expanded.csv"),
+                    "toxicity_label": "toxicity",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filename, toxicity_label):
+        """Yields examples.
+
+        Each example contains a text input and then seven annotation labels.
+
+        Args:
+          filename: the path of the file to be read for this split.
+          toxicity_label: indicates 'target' or 'toxicity' to capture the variation
+            in the released labels for this dataset.
+
+        Yields:
+          A dictionary of features, all floating point except the input text.
+        """
+        with open(filename, encoding="utf-8") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                example = {}
+                example["text"] = row["comment_text"]
+                example["toxicity"] = float(row[toxicity_label])
+                for label in ["severe_toxicity", "obscene", "threat", "insult", "identity_attack", "sexual_explicit"]:
+                    example[label] = float(row[label])
+                yield row["id"], example

dataset_infos.json

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+{"default": {"description": "\nThe comments in this dataset come from an archive of the Civil Comments\nplatform, a commenting plugin for independent news sites. These public comments\nwere created from 2015 - 2017 and appeared on approximately 50 English-language\nnews sites across the world. When Civil Comments shut down in 2017, they chose\nto make the public comments available in a lasting open archive to enable future\nresearch. The original data, published on figshare, includes the public comment\ntext, some associated metadata such as article IDs, timestamps and\ncommenter-generated \"civility\" labels, but does not include user ids. Jigsaw\nextended this dataset by adding additional labels for toxicity and identity\nmentions. This data set is an exact replica of the data released for the\nJigsaw Unintended Bias in Toxicity Classification Kaggle challenge.  This\ndataset is released under CC0, as is the underlying comment text.\n", "citation": "\n@article{DBLP:journals/corr/abs-1903-04561,\n  author    = {Daniel Borkan and\n               Lucas Dixon and\n               Jeffrey Sorensen and\n               Nithum Thain and\n               Lucy Vasserman},\n  title     = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text\n               Classification},\n  journal   = {CoRR},\n  volume    = {abs/1903.04561},\n  year      = {2019},\n  url       = {http://arxiv.org/abs/1903.04561},\n  archivePrefix = {arXiv},\n  eprint    = {1903.04561},\n  timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},\n  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},\n  bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "toxicity": {"dtype": "float32", "id": null, "_type": "Value"}, "severe_toxicity": {"dtype": "float32", "id": null, "_type": "Value"}, "obscene": {"dtype": "float32", "id": null, "_type": "Value"}, "threat": {"dtype": "float32", "id": null, "_type": "Value"}, "insult": {"dtype": "float32", "id": null, "_type": "Value"}, "identity_attack": {"dtype": "float32", "id": null, "_type": "Value"}, "sexual_explicit": {"dtype": "float32", "id": null, "_type": "Value"}}, "supervised_keys": {"input": "text", "output": "toxicity"}, "builder_name": "civil_comments", "config_name": "default", "version": {"version_str": "0.9.0", "description": null, "datasets_version_to_prepare": null, "major": 0, "minor": 9, "patch": 0}, "splits": {"test": {"name": "test", "num_bytes": 32073013, "num_examples": 97320, "dataset_name": "civil_comments"}, "train": {"name": "train", "num_bytes": 596835730, "num_examples": 1804874, "dataset_name": "civil_comments"}, "validation": {"name": "validation", "num_bytes": 32326369, "num_examples": 97320, "dataset_name": "civil_comments"}}, "download_checksums": {"https://storage.googleapis.com/jigsaw-unintended-bias-in-toxicity-classification/civil_comments.zip": {"num_bytes": 414947977, "checksum": "767b71a3d9dc7a2eceb234d0c3e7e38604e11f59c12ba1cbb888ffd4ce6b6271"}}, "download_size": 414947977, "dataset_size": 661235112, "size_in_bytes": 1076183089}}

dummy/0.9.0/dummy_data.zip

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+oid sha256:f645cdf6b361fb222d20cffcdcad61a1ad5a012122cd1f0470415f6bedfff3c0
+size 1928